Energy cost minimization with job security guarantee in Internet data center

With the proliferation of various big data applications and resource demand from Internet data centers (IDCs), the energy cost has been skyrocketing, and it attracts a great deal of attention and brings many energy optimization management issues. However, the security problem for a wide range of applications, which has been overlooked, is another critical concern and even ranked as the greatest challenge in IDC. In this paper, we propose an energy cost minimization (ECM) algorithm with job security guarantee for IDC in deregulated electricity markets. Randomly arriving jobs are routed to a FIFO queue, and a heuristic algorithm is devised to select security levels for guaranteeing job risk probability constraint. Then, the energy optimization problem is formulated by taking the temporal diversity of electricity price into account. Finally, an online energy cost minimization algorithm is designed to solve the problem by Lyapunov optimization framework which offers provable energy cost optimization and delay guarantee. This algorithm can aggressively and adaptively seize the timing of low electricity price to process workloads and defer delay-tolerant workloads execution when the price is high. Based on the real-life electricity price, simulation results prove the feasibility and effectiveness of proposed algorithm

From geographically dispersed data centers towards hierarchical edge computing

Internet scale data centers are generally dispersed in different geographical regions. While the main goal of deploying the geographically dispersed data centers is to provide redundancy, scalability and high availability, the geographic dispersity provides another opportunity for efficient employment of global resources, e.g., utilizing price-diversity in electricity markets or utilizing locational diversity in renewable power generation. In other words, an efficient approach for geographical load balancing (GLB) across geo-dispersed data centers not only can maximize the utilization of green energy but also can minimize the cost of electricity. However, due to the different costs and disparate environmental impacts of the renewable energy and brown energy, such a GLB approach should tap on the merits of the separation of green energy utilization maximization and brown energy cost minimization problems. To this end, the notion of green workload and green service rate, versus brown workload and brown service rate, respectively, to facilitate the separation of green energy utilization maximization and brown energy cost minimization problems is proposed. In particular, a new optimization framework to maximize the profit of running geographically dispersed data centers based on the accuracy of the G/D/1 queueing model, and taking into consideration of multiple classes of service with individual service level agreement deadline for each type of service is developed. A new information flow graph based model for geo-dispersed data centers is also developed, and based on the developed model, the achievable tradeoff between total and brown power consumption is characterized. Recently, the paradigm of edge computing has been introduced to push the computing resources away from the data centers to the edge of the network, thereby reducing the communication bandwidth requirement between the sources of data and the data centers. However, it is still desirable to investigate how and where at the edge of the network the computation resources should be provisioned. To this end, a hierarchical Mobile Edge Computing (MEC) architecture in accordance with the principles of LTE Advanced backhaul network is proposed and an auction-based profit maximization approach which effectively facilitates the resource allocation to the subscribers of the MEC network is designed. A hierarchical capacity provisioning framework for MEC that optimally budgets computing capacities at different hierarchical edge computing levels is also designed. The proposed scheme can efficiently handle the peak loads at the access point locations while coping with the resource poverty at the edge. Moreover, the code partitioning problem is extended to a scheduling problem over time and the hierarchical mobile edge network, and accordingly, a new technique that leads to the optimal code partitioning in a reasonable time even for large-sized call trees is proposed. Finally, a novel NOMA augmented edge computing model that captures the gains of uplink NOMA in MEC users\u27 energy consumption is proposed